{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-34-11702066605e>:15: MatplotlibDeprecationWarning: \n",
      "The mpl_toolkits.axes_grid module was deprecated in Matplotlib 2.1 and will be removed two minor releases later. Use mpl_toolkits.axes_grid1 and mpl_toolkits.axisartist, which provide the same functionality instead.\n",
      "  from mpl_toolkits.axes_grid.inset_locator import (inset_axes, InsetPosition,\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "import math\n",
    "import argparse\n",
    "from augerino import datasets, models, losses\n",
    "import glob\n",
    "import re\n",
    "import pandas as pd\n",
    "import sys\n",
    "sys.path.append(\"../learning-invariance/\")\n",
    "from data.generate_data import *\n",
    "from loss_getters import compute_loss\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.axes_grid.inset_locator import (inset_axes, InsetPosition,\n",
    "                                                  mark_inset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "softplus = torch.nn.Softplus()\n",
    "savedir = \"./saved-outputs/\"\n",
    "\n",
    "epochs = 20\n",
    "reg = 0.05\n",
    "## stanard running procedure ##\n",
    "net = models.SimpleConv(c=32, num_classes=4)\n",
    "augerino = models.UniformAug()\n",
    "model = models.AugAveragedModel(net, augerino,ncopies=1)\n",
    "\n",
    "ntrain = 10000\n",
    "ntest = 5000\n",
    "\n",
    "trainloader, testloader = generate_mario_data(ntrain=ntrain, ntest=ntest,\n",
    "                                              batch_size=128, dpath=\"../limit-invariance/data/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_widths = torch.ones(6) * -5.\n",
    "\n",
    "model.aug.set_width(start_widths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = torch.optim.Adam(model.parameters(),lr=0.01,\n",
    "                            weight_decay=1e-3)\n",
    "use_cuda = torch.cuda.is_available()\n",
    "if use_cuda:\n",
    "    model = model.cuda()\n",
    "\n",
    "logged_aug = []\n",
    "\n",
    "criterion = losses.unif_aug_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch  0\n",
      "epoch  1\n",
      "epoch  2\n",
      "epoch  3\n",
      "epoch  4\n",
      "epoch  5\n",
      "epoch  6\n",
      "epoch  7\n",
      "epoch  8\n",
      "epoch  9\n",
      "epoch  10\n",
      "epoch  11\n",
      "epoch  12\n",
      "epoch  13\n",
      "epoch  14\n",
      "epoch  15\n",
      "epoch  16\n",
      "epoch  17\n",
      "epoch  18\n",
      "epoch  19\n"
     ]
    }
   ],
   "source": [
    "count = 0\n",
    "for epoch in range(epochs):  # loop over the dataset multiple times\n",
    "    for i, data in enumerate(trainloader):\n",
    "        count += 1\n",
    "        if count < 100:\n",
    "            \n",
    "            # get the inputs; data is a list of [inputs, labels]\n",
    "            inputs, labels = data\n",
    "\n",
    "            if use_cuda:\n",
    "                inputs, labels = inputs.cuda(), labels.cuda()\n",
    "\n",
    "            # zero the parameter gradients\n",
    "            optimizer.zero_grad()\n",
    "\n",
    "            # forward + backward + optimize\n",
    "            # print(inputs.shape)\n",
    "            outputs = model(inputs)\n",
    "            loss = criterion(outputs, labels, model,\n",
    "                            reg=reg)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "            log = softplus(model.aug.width).tolist()\n",
    "            log += model.aug.width.grad.data.tolist()\n",
    "            log += [loss.item(), count]\n",
    "            logged_aug.append(log)\n",
    "    print(\"epoch \", epoch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now Compute the Gradients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "logger = pd.DataFrame(logged_aug)\n",
    "cols = ['width' + str(i) for i in range(6)]\n",
    "cols += ['grad' + str(i) for i in range(6)]\n",
    "cols += ['loss', 'iter']\n",
    "logger.columns = cols\n",
    "logger = logger.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y = next(iter(trainloader))\n",
    "x = x.cuda()\n",
    "y = y.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_widths = model.aug.width.data.clone()\n",
    "widths = torch.linspace(-5, 5, 20)\n",
    "grads = torch.zeros_like(widths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "grad_logger = []\n",
    "for batch_idx, batch in enumerate(trainloader):\n",
    "    if batch_idx < 25:\n",
    "        x, y = batch\n",
    "        x, y = x.cuda(), y.cuda()\n",
    "        for idx, ww in enumerate(widths):\n",
    "            optimizer.zero_grad()\n",
    "            temp_widths = train_widths.clone()\n",
    "            temp_widths[2] = ww\n",
    "            model.aug.set_width(temp_widths.cuda())\n",
    "            preds = model(x)\n",
    "\n",
    "            acc = torch.sum(torch.argmax(preds, -1) == y)\n",
    "\n",
    "        #     loss = loss_func(convnet.model(x), y, convnet.model, reg=0.1)\n",
    "            loss = criterion(model(x), y, model,\n",
    "                                reg=reg)\n",
    "\n",
    "            loss.backward()\n",
    "\n",
    "            grad_logger.append([batch_idx, ww.item(), model.aug.width.grad[2].item(), acc.item()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "log = pd.DataFrame(grad_logger)\n",
    "log.columns = ['idx', 'width', 'grad', 'acc']\n",
    "\n",
    "log['sp_width'] = softplus(torch.tensor(log['width']))\n",
    "log['acc'] = log['acc']/128."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"white\")\n",
    "lwd=3.\n",
    "fs=18\n",
    "sns.lineplot(x='sp_width', y='acc', data=log,\n",
    "            linewidth=lwd)\n",
    "plt.xlabel(\"Rotation Distribution Width\", fontsize=fs)\n",
    "plt.ylabel(\"Batch Accuracy\", fontsize=fs)\n",
    "sns.despine()\n",
    "plt.tick_params(\"both\", labelsize=fs-4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 5), dpi=100)\n",
    "\n",
    "sns.set_style(\"white\")\n",
    "sns.lineplot(x='sp_width', y='grad', data=log,\n",
    "            linewidth=lwd, ax=ax)\n",
    "ax.set_xlabel(\"Rotation Distribution Width\", fontsize=fs)\n",
    "ax.set_ylabel(\"Gradient\", fontsize=fs)\n",
    "ax.tick_params(\"both\", labelsize=fs-4)\n",
    "sns.despine()\n",
    "\n",
    "ax2 = plt.axes([0.2,0.2, 0.01,0.01])\n",
    "ip = InsetPosition(ax, [0.08,0.45,0.5,0.5])\n",
    "ax2.set_axes_locator(ip)\n",
    "mark_inset(ax, ax2, loc1=2, loc2=4, fc=\"none\", ec='0.5')\n",
    "sns.lineplot(x='sp_width', y='grad', data=log[(log['sp_width']< 1.2) & (log['sp_width'] >0.05)])\n",
    "sns.despine()\n",
    "# ax2.axhline(0)\n",
    "ax2.set_xticks([])\n",
    "ax2.set_yticks([])\n",
    "ax2.set_xlabel(\"\")\n",
    "ax2.set_ylabel(\"\")\n",
    "ax.set_xlim(0, 4.4)\n",
    "plt.savefig(\"./grad_example.png\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
